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Simple, Pythonic text processing. Sentiment analysis, POS tagging, noun phrase parsing, and more.

Project description

TextBlob: Simplified Text Processing

Travis-CI Number of PyPI downloads

Homepage: https://textblob.readthedocs.org/

TextBlob is a Python (2 and 3) library for processing textual data. It provides a consistent API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, and more.

from text.blob import TextBlob

text = '''
The titular threat of The Blob has always struck me as the ultimate movie
monster: an insatiably hungry, amoeba-like mass able to penetrate
virtually any safeguard, capable of--as a doomed doctor chillingly
describes it--"assimilating flesh on contact.
Snide comparisons to gelatin be damned, it's a concept with the most
devastating of potential consequences, not unlike the grey goo scenario
proposed by technological theorists fearful of
artificial intelligence run rampant.
'''

blob = TextBlob(text)
blob.pos_tags       # [(Word('The'), u'DT'), (Word('titular'), u'JJ'),
                    #  (Word('threat'), u'NN'), ...])
blob.noun_phrases   # WordList(['titular threat', 'blob',
                    #            'ultimate movie monster',
                    #            'amoeba-like mass', ...])

for sentence in blob.sentences:
    print(blob.sentiment)
# (0.060, 0.605)
# (-0.34, 0.77)

Get it now

$ pip install -U textblob
$ curl https://raw.github.com/sloria/TextBlob/master/download_corpora.py | python

Documentation

Hosted here at ReadTheDocs.

Requirements

  • Python >= 2.6 or >= 3.3

Testing

Run

python run_tests.py

to run all tests.

License

TextBlob is licenced under the MIT license. See the bundled LICENSE file for more details.

Changelog

0.4.0 (unreleased)

  • New tokenizer module with WordTokenizer and SentenceTokenizer. Both textblob and NLTK tokenizer objects and be passed to TextBlob’s constructor. Tokens are accessed through the new tokens property.
  • New Blobber class for creating TextBlobs that share the same tagger, tokenizer, and np_extractor.
  • Backwards-incompatible: TextBlob.json() is now a method, not a property. This allows you to pass arguments (the same that you would pass to json.dumps()).
  • New home for documentation: https://textblob.readthedocs.org/
  • Fix bug with adding blobs to bytestrings.

0.3.10 (2013-08-02)

  • Bundled NLTK no longer overrides local installation.
  • Fix sentiment analysis of text with non-ascii characters.

0.3.9 (2013-07-31)

  • Updated nltk.
  • ConllExtractor is now Python 3-compatible.
  • Improved sentiment analysis.
  • Blobs are equal (with ==) to their string counterparts.
  • Added instructions to install textblob without nltk bundled.
  • Dropping official 3.1 and 3.2 support.

0.3.8 (2013-07-30)

  • Importing TextBlob is now much faster. This is because the noun phrase parsers are trained only on the first call to noun_phrases (instead of training them every time you import TextBlob).
  • Add text.taggers module which allows user to change which POS tagger implementation to use. Currently supports PatternTagger and NLTKTagger (NLTKTagger only works with Python 2).
  • NPExtractor and Tagger objects can be passed to TextBlob’s constructor.
  • Fix bug with POS-tagger not tagging one-letter words.
  • Rename text/np_extractor.py -> text/np_extractors.py
  • Add run_tests.py script.

0.3.7 (2013-07-28)

  • Every word in a Blob or Sentence is a Word instance which has methods for inflection, e.g word.pluralize() and word.singularize().
  • Updated the np_extractor module. Now has an new implementation, ConllExtractor that uses the Conll2000 chunking corpus. Only works on Py2.

Project details


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